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<p>Climate change has profoundly altered global and regional hydro-climatic systems, leading to increased variability in precipitation and temperature patterns. Consequently, recurrent occurrences of extreme events have been observed in most parts of the world. Among these, drought is one of the most frequent and adverse natural phenomena, having severe impacts on ecosystems, agriculture, and water resources. Therefore, continuous monitoring of drought events is essential for effective mitigation and adaptation policies. However, accurate and robust drought quantification remains a major scientific challenge, as conventional Standardized Drought Indices (SDIs) such as SPI, SPEI, and SPTI often rely on fixed-distributional assumptions that fail to capture the evolving and multimodal nature of hydro-meteorological data. To address this limitation, this study introduces a novel probabilistic framework termed the <i>Varying Component Gaussian Mixture Distribution–Drought Assessment Framework (VCGMD–DAF)</i>. The proposed method adaptively determines the optimal number of Gaussian mixture components using the Bayesian Information Criterion (BIC), thereby enabling the model to flexibly represent nonstationary climatic behaviors and complex data structures. Unlike traditional approaches that assume a fixed probabilistic form, the VCGMD–DAF dynamically adjusts its mixture structure according to the underlying data, enhancing both the accuracy and stability of drought standardization. The framework is applied to multi-temporal meteorological data from six northern stations in Pakistan to evaluate its performance in modeling probabilistic drought dynamics. The results of this research demonstrate that the VCGMD–DAF significantly improves the precision of drought detection and reduces uncertainty in drought monitoring compared to conventional SDI models. Across all stations, the K-component Gaussian mixture distributions yield substantially lower AIC and BIC values (approximately -2,934.77 to -5,426.25) compared to conventional univariate distributions (approximately -4.60 to -1,157.84), demonstraextent of the drought condition (Johnson eting a markedly improved probabilistic representation of hydroclimatic variability and extremes. In general, this adaptive mixture-based approach provides a statistically rigorous and computationally efficient foundation for the assessment of drought risk and supports the development of resilient water resource management and climate adaptation strategies.</p>

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A novel varying component gaussian mixture model for improved drought assessment based on standardized-type drought indices

  • 期刊论文

  • 发表日期：

    2026年5月23日

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  • Muhammad Ismail,
  • Muhammad Mohsin

摘要

Climate change has profoundly altered global and regional hydro-climatic systems, leading to increased variability in precipitation and temperature patterns. Consequently, recurrent occurrences of extreme events have been observed in most parts of the world. Among these, drought is one of the most frequent and adverse natural phenomena, having severe impacts on ecosystems, agriculture, and water resources. Therefore, continuous monitoring of drought events is essential for effective mitigation and adaptation policies. However, accurate and robust drought quantification remains a major scientific challenge, as conventional Standardized Drought Indices (SDIs) such as SPI, SPEI, and SPTI often rely on fixed-distributional assumptions that fail to capture the evolving and multimodal nature of hydro-meteorological data. To address this limitation, this study introduces a novel probabilistic framework termed the Varying Component Gaussian Mixture Distribution–Drought Assessment Framework (VCGMD–DAF). The proposed method adaptively determines the optimal number of Gaussian mixture components using the Bayesian Information Criterion (BIC), thereby enabling the model to flexibly represent nonstationary climatic behaviors and complex data structures. Unlike traditional approaches that assume a fixed probabilistic form, the VCGMD–DAF dynamically adjusts its mixture structure according to the underlying data, enhancing both the accuracy and stability of drought standardization. The framework is applied to multi-temporal meteorological data from six northern stations in Pakistan to evaluate its performance in modeling probabilistic drought dynamics. The results of this research demonstrate that the VCGMD–DAF significantly improves the precision of drought detection and reduces uncertainty in drought monitoring compared to conventional SDI models. Across all stations, the K-component Gaussian mixture distributions yield substantially lower AIC and BIC values (approximately -2,934.77 to -5,426.25) compared to conventional univariate distributions (approximately -4.60 to -1,157.84), demonstraextent of the drought condition (Johnson eting a markedly improved probabilistic representation of hydroclimatic variability and extremes. In general, this adaptive mixture-based approach provides a statistically rigorous and computationally efficient foundation for the assessment of drought risk and supports the development of resilient water resource management and climate adaptation strategies.